Affiliation:
1. Central South University China and Xiangjiang Laboratory, China
2. Central South University, China
3. Hunan University of Technology and Business, China and Xiangjiang Laboratory, China
Abstract
The spread of misinformation on social media is a serious issue that can have negative consequences for public health and political stability. While detecting and identifying misinformation can be challenging, many attempts have been made to address this problem. However, traditional models that focus on pairwise relationships on misinformation propagation paths may not be effective in capturing the underlying connections among multiple tweets. To address this limitation, the proposed “Conversation-Branch-Tweet” hypergraph convolutional network (CBT-HGCN) uses a hypergraph to represent the internal structure and content of tweet data, with tweets and their replies viewed as nodes and hyperedges, respectively. The model first pre-processes the tweets of a conversation and then uses a pre-trained model as an encoder to extract node information. Finally, a hypergraph convolution network is used as an information fuser for classification. Experimental results on three benchmark datasets (Twitter15, Twitter16, and Pheme) show that the proposed model outperforms several strong baseline models and achieves state-of-the-art performance. This indicates that the CBT-HGCN approach is effective in detecting and identifying misinformation on social media by capturing the underlying connections among multiple tweets.
Funder
Open Project of Xiangjiang Laboratory
National Key Research and Development Program of China
National Natural Science Foundation of China
Hunan Provincial Natural Science Foundation of China
Changsha Municipal Natural Science Foundation
Training Program for Excellent Young Innovators of Changsha
The science and technology innovation Program of Hunan Province
Industry-University-Research Innovation Fund of Chinese University
Publisher
Association for Computing Machinery (ACM)
Reference40 articles.
1. Hypergraph convolution and hypergraph attention
2. Peter W. Battaglia Jessica B. Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinícius Flores Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner Çaglar Gülçehre H. Francis Song Andrew J. Ballard Justin Gilmer George E. Dahl Ashish Vaswani Kelsey R. Allen Charles Nash Victoria Langston Chris Dyer Nicolas Heess Daan Wierstra Pushmeet Kohli Matthew M. Botvinick Oriol Vinyals Yujia Li and Razvan Pascanu. 2018. Relational inductive biases deep learning and graph networks. arXiv:1806.01261. Retrieved from https://arxiv.org/abs/1806.01261
3. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding
4. Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
5. Causal Understanding of Fake News Dissemination on Social Media